The concept of knowledge graphs arose from scientific advances in a variety of research fields, including the semantic web, databases, natural language processing, and machine learning. According to ...

Knowledge graphs and ontologies form the backbone of the Semantic Web by enabling the structured representation and interconnection of data across diverse domains. These frameworks allow for the ...

For decades, enterprise data infrastructure focused on answering the question: “What happened in our business?” Business intelligence tools, data warehouses, and pipelines were built to surface ...

What if you could transform overwhelming, disconnected datasets into a living, breathing map of relationships, one that not only organizes your data but also reveals insights you didn’t even know you ...

Knowledge graphs and semantic technologies have emerged as transformative tools in geoscience, offering a structured means to integrate and interpret diverse data streams. These methodologies support ...

The KM and knowledge graph (KG) communities have detected each other, but so far, there has been little integration or alignment. Both communities have so much to offer each other. The KG community ...

Unpacking the next data platform is a crucial process in the constantly changing world of data and artificial intelligence. It involves understanding metadata knowledge graphs and how different layers ...

Generative AI depends on data to build responses to user queries. Training large language models (LLMs) uses huge volumes of data—for example, OpenAI’s GPT-3 used the CommonCrawl data set, which stood ...

Latest Graphwise offering bridges the gap between complex enterprise data and functional AI agents, using ontologies reduces inaccurate answers 2X in benchmarksNEW YORK, Feb. 16, 2026 /PRNewswire/ ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...